The Influence of Pay-To-Play Fees on Participation in Interscholastic Sports: A School-Level Analysis of Michigan’s Public Schools
Bibliographic record
Abstract
BACKGROUND: School districts in the United States are turning toward new sources of revenue to maintain their interscholastic sports programs. One common revenue generating policy is the implementation of participation fees, also known as pay-to-play. One concern of the growing trend of participation fees is how it impacts student participation opportunities. This study looks at how pay-to-play fees are impacting participation opportunities and participation rates in the state of Michigan. METHODS: Through merging 3 school-level data sets, Civil Rights Data Collection, the Common Core of Data, and participation information from MHSAA (Michigan High School Athletic Association), bivariate analysis and ordinary least squares regression were used in our analysis. RESULTS: Our findings indicate that certain types of schools are able to support pay-to-play fees: relatively large schools that are located in suburban, white communities, with relatively low poverty rates. We also found that participation fees are not decreasing the number of sport opportunities for students, participation opportunities are higher in schools with fees; but participation rates are similar between schools with and without participation fees. CONCLUSIONS: Participation fee policy implications are discussed and we offer suggestions for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".